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578 results for “horizons”
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
<p>Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.</p>
Soil physical and chemical properties based on genetic horizon from 4 replicate pits placed around the replicate LTER control plots sampled in 1988 and 1989.
Dataset contains the following soil properties for each genetic horizon - site, Soil pit, upper and lower boundary (cm), Mg meq/100gm, Ca meq/100gm, K meq/100gm, CEC meq/100gm, pH, %C, %sand, %silt, %clay, Total %N, Total %P, % organic matter, Mn meq/100gm, Available-P ppm, %CO3, bulk density gm/cm3, Volume wt gm/m2.
Comparison of polyphenol degrading enzyme activities between forest types and soil horizons from 2003 to 2004
In the southern Appalachians Rhododendron maximum thickets suppress conifer and hardwood regeneration. While there has been research on the effects of R. maximum on physical and chemical environment, the functioning of R. maximum ericoid mycorrhizas has been unexplored. The litter of ericaceous plants tends to be rich in phenolic compounds. These compounds can form recalcitrant complexes with various forms of organic N, and may be responsible for lowering decomposition and N mineralization rates. While polyphenol-organic N complexes are highly recalcitrant, some fungi, particularly ericoid mycorrhizal fungi, have the ability to access this sequestered N. Since the litter of ericaceous plants is rich in phenolic compounds and ericoid mycorrhizal fungi are equipped to degrade phenolic compounds, polyphenol-organic N complexing may represent an N cycling strategy that prevents non-ericaceous plants from accessing sources of organic N. We propose to examine the activities of polyphenol degrading enzymes in the soil of R. maximum thickets and neighboring hardwood forests.
Snow horizon chemistry data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.
Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles in order to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season. Snow samples were collected and analyzed for cations and anions at the Mountain Research Station's Arikaree (formerly Kiowa) Laboratory.
Radiocarbon Data from Middle Horizon Sites in the Upper Majes Valley, Arequipa Peru
<p>In 2019, fifteen AMS radiocarbon dates were obtained across four Middle Horizon (AD 600-1000) sites located in the upper Majes Valley in Arequipa, Peru. These include Wari contexts from the sites Pakaytambo and El Tambo, and local road waystations of Santa Rosa II and La Angostura. Dating of both construction and abandonment contexts indicate these sites correspond to the late Middle Horizon, a time of intense connectivity in the prehistoric Andes and Wari state expansionism.</p> <p>This data is a component of the PhD dissertation entitled <em>Networks of Empire: The Role of Infrastructure in Wari State Expansion in Arequipa, Peru (AD 600-1000)</em> by David A. Reid (University of Illinois at Chicago).</p> <p>Funding was provided by the National Science Foundation’s Doctoral Dissertation Research Improvement Award (#1854651).</p>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
<p>In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.</p> <p>We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.</p> <p>Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production.</p> <p>Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. </p>
NYMPHE Horizon 2020 project Falasarna (GR) Test Sites preliminary data
<p>The dataset is a preliminary vision on the Falasarna (GR) test site for bioremediation action in the EU funded Horizon 2020 project. The data set iwill be used to futher refine the datasets structure and to define the hyerachical data dependences. The distribution of the typical species for phrigana habitat was investigated. The data were obtain prior application of the bioremediation measures. The data collection mission took place in October 2024. The valuee were obtaiing by direct measurement on site, </p>
Lithogenesis of a phosphatized tephra marker horizon in the Eocene Messel maar lake
<p>Diffractograms of the Messel pit marker horizon M - messelite bands and a band consisting of messelite, montgomeryite, and mantienneite</p>
Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem". in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem".
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany).
Dataset: Horizon Bancorp, Inc. (HBNC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Kinetics SPAC Active ETF (SPAQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Kinetics Medical ETF (MEDX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Space Acquisition I Corp. (HSPOW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Space Acquisition I Corp. (HSPOU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Space Acquisition I Corp. (HSPOR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Horizon Space Acquisition I Corp. (HSPO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New Horizon Aircraft Ltd. (HOVRW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New Horizon Aircraft Ltd. (HOVR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.